Biology subjects

Rhim, I.

Publications and source records attributed to Rhim, I..

3 recordsLinked to original sources

bioRxiv2023

What humans learn from corrective feedback for perceptual decision-making

Corrective feedback received on perceptual decisions is crucial for adjusting decision-making strategies to improve future choices. However, its complex interaction with other decision components, such as previous stimuli and choices, challenges a principled account of how it shapes subsequent decisions. One popular approach, based on animal behavior and extended to human perceptual decision-making, employs reinforcement learning, a principle proven successful in reward-based decision-making. The core idea behind this approach is that decision-makers, although engaged in a perceptual task, treat corrective feedback as rewards from which they learn choice values. Here, we explore an alternative idea, which is that humans consider corrective feedback on perceptual decisions as evidence of the actual state of the world rather than as rewards for their choices. By implementing these feedback-as-reward and feedback-as-evidence hypotheses on a shared learning platform, we show that the latter outperforms the former in explaining how corrective feedback adjusts the decision-making strategy along with past stimuli and choices. Our work suggests that humans learn about what has happened in their environment rather than the values of their own choices through corrective feedback during perceptual decision-making.

neuroscience↗

bioRxiv2021

Inseparable color and form in mouse visual cortex described by random pooling from rods and cones

Spatial transitions in color can aid any visual perception task, and its neural representation - the "integration of color and form" - is thought to begin at primary visual cortex (V1). An integration of color and form is untested in mouse V1, yet studies show that the ventral retina provides the necessary substrate from green-sensitive rods and UV-sensitive cones. Here, we used two-photon imaging in V1 to measure spatial frequency (SF) tuning along four axes of rod and cone contrast space, including luminance and color. We first reveal that V1s sensitivity to color is similar to luminance, yet average SF tuning is significantly shifted lowpass for color. Next, guided by linear models, we used SF tuning along all four color axes to estimate the proportion of neurons that fall into classic models of color opponency - "single-", "double-", and "non-opponent". Few neurons (~6%) fit criteria for double-opponency, which are uniquely tuned for chromatic borders. Most of the population can be described as a unimodal distribution ranging from strongly single-opponent to non-opponent. Consistent with recent studies of the rodent and primate retina, our V1 data is well-described by a simple model in which ON and OFF channels to V1 sample the photoreceptor mosaic randomly.

neuroscience↗

bioRxiv2020

Variations in photoreceptor throughput to mouse visual cortex and the unique effects on tuning

Visual input to primary visual cortex (V1) depends on highly adaptive filtering in the retina. In turn, isolation of V1 computations to study cortical circuits requires control over retinal adaption and its corresponding spatio-temporal-chromatic output. Here, we first measure the balance of input to V1 from the three main photoreceptor opsins - M-opsin, S-opsin, and rhodopsin - as a function of light adaption and retinotopy. Results show that V1 is rod-mediated in common laboratory settings, yet cone-mediated in natural daylight, as evidenced by exclusive sensitivity to UV wavelengths via cone S-opsin in the upper visual field. Next, we show that cone-mediated V1 responds to 2.5-fold higher temporal frequencies than rod-mediated V1. Furthermore, cone-mediated V1 has smaller RFs, yet similar spatial frequency tuning. V1 responses in rod-deficient (Gnat1-/-) mice confirm that the effects are due to differences in photoreceptor contribution. This study provides foundation for using mouse V1 to study cortical circuits.

neuroscience↗